The ability to more efficiently detect and analyze specific components (analytes) of a mixture or sample would greatly benefit medicine, environmental analysis, and consumer industries (e.g., food analysis), to name a few. For example, the food industry depends upon chemical analysis for quality control, environmentalists depend upon chemical analysis for the detection of harmful agents in natural resources, such as water, and the medical community depends upon analysis for the detection of agents such as metabolites, drugs, and glucose to name a few. Although many methods suitable for sensing applications have been developed (see, for example, Wolfbeis et al., Analytica Chim. Acta, 1991, 250, 181), there still remains a need to develop chemical sensors that are capable of detecting analytes with specificity and selectivity.
In general, a sensor device includes the following: 1) a recognition element capable of identifying and interacting with the analyte which usually is contained in low concentration in a mixture of a variety of other components; 2) a transducer element that can transform the recognition process into a measurable signal; and 3) a processing unit, which, after amplification of the primary signal, converts it into a familiar readout (e.g., pH, ppm, etc.). One approach that has been utilized in the development of more selective sensors is the use of bioorganic species (enzymes, ion carriers, and natural or synthetic receptor/carriers) that are believed to mimic the selectivity of nature and undergo specific reactions with the entity to be recognized, resulting in specific recognition and, consequently, sensing. The difficulty with this approach, however, is that identification of agents that can selectively interact with analytes of interest can be problematic. For example, synthetic receptors often exhibit poor selectivity, and have difficulties in transducing the recognition process. Additionally, these approaches have generally focused on the interaction of one specific agent with one analyte, creating a cumbersome system if many analytes need to be detected. It would thus be desirable to develop a system that would minimize the number of recognition elements necessary, whereby the recognition elements utilized would be cross-reactive, thus each interacting with more than one analyte to generate a unique agent and/or change that can be readily detected.
Towards this end, Walt et al. (see, for example, Dickinson et al., Anal. Chem. 1999, 71, 2192; White et al., Anal. Chem. 1996, 68, 2191; Dickinson et al., Nature 1996, 382, 697) described a novel approach, the “artificial nose”, in which high-density optical arrays that directly incorporate a number of structural and operational features of the olfactory system were developed for the cross-reactive analysis of vapors. Clearly, it would also be desirable if an efficient and sensitive cross-reactive sensor system could be developed for the analysis of liquid analytes, preferably in an array format for high-throughput complex analysis.